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Forecasting the magnitude of the largest expected earthquake

Author

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  • Robert Shcherbakov

    (University of Western Ontario
    University of Western Ontario)

  • Jiancang Zhuang

    (Institute of Statistical Mathematics)

  • Gert Zöller

    (University of Potsdam)

  • Yosihiko Ogata

    (Institute of Statistical Mathematics)

Abstract

The majority of earthquakes occur unexpectedly and can trigger subsequent sequences of events that can culminate in more powerful earthquakes. This self-exciting nature of seismicity generates complex clustering of earthquakes in space and time. Therefore, the problem of constraining the magnitude of the largest expected earthquake during a future time interval is of critical importance in mitigating earthquake hazard. We address this problem by developing a methodology to compute the probabilities for such extreme earthquakes to be above certain magnitudes. We combine the Bayesian methods with the extreme value theory and assume that the occurrence of earthquakes can be described by the Epidemic Type Aftershock Sequence process. We analyze in detail the application of this methodology to the 2016 Kumamoto, Japan, earthquake sequence. We are able to estimate retrospectively the probabilities of having large subsequent earthquakes during several stages of the evolution of this sequence.

Suggested Citation

  • Robert Shcherbakov & Jiancang Zhuang & Gert Zöller & Yosihiko Ogata, 2019. "Forecasting the magnitude of the largest expected earthquake," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11958-4
    DOI: 10.1038/s41467-019-11958-4
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    Cited by:

    1. Tomlinson, Matthew F. & Greenwood, David & Mucha-Kruczyński, Marcin, 2024. "2T-POT Hawkes model for left- and right-tail conditional quantile forecasts of financial log returns: Out-of-sample comparison of conditional EVT models," International Journal of Forecasting, Elsevier, vol. 40(1), pages 324-347.
    2. Jiaqi Zhang & Xijun He, 2023. "Earthquake magnitude prediction using a VMD-BP neural network model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 189-205, May.

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